Merge remote-tracking branch 'upstream/master' into scipy19-docs

* upstream/master: (43 commits)
  Add hypothesis support to related projects (#3335)
  More doc fixes (#3333)
  Improve the documentation of swap_dims (#3331)
  fix the doc names of the return value of swap_dims (#3329)
  Fix isel performance regression (#3319)
  Allow weakref (#3318)
  Clarify that "scatter" is a plotting method in what's new. (#3316)
  Fix whats-new date :/
  Revert to dev version
  Release v0.13.0
  auto_combine deprecation to 0.14 (#3314)
  Deprecation: groupby, resample default dim. (#3313)
  Raise error if cmap is list of colors (#3310)
  Refactor concat to use merge for non-concatenated variables (#3239)
  Honor `keep_attrs` in DataArray.quantile (#3305)
  Fix DataArray api doc (#3309)
  Accept int value in head, thin and tail (#3298)
  ignore h5py 2.10.0 warnings and fix invalid_netcdf warning test. (#3301)
  Update why-xarray.rst with clearer expression (#3307)
  Compat and encoding deprecation to 0.14 (#3294)
  ...

asv_bench/benchmarks/combine.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 import xarray as xr
 
 

asv_bench/benchmarks/indexing.py

@@ -125,3 +125,16 @@ def setup(self, key):
         requires_dask()
         super().setup(key)
         self.ds = self.ds.chunk({"x": 100, "y": 50, "t": 50})
+
+
+class BooleanIndexing:
+    # https://github.com/pydata/xarray/issues/2227
+    def setup(self):
+        self.ds = xr.Dataset(
+            {"a": ("time", np.arange(10_000_000))},
+            coords={"time": np.arange(10_000_000)},
+        )
+        self.time_filter = self.ds.time > 50_000
+
+    def time_indexing(self):
+        self.ds.isel(time=self.time_filter)

doc/api.rst

@@ -8,7 +8,7 @@ This page provides an auto-generated summary of xarray's API. For more details
 and examples, refer to the relevant chapters in the main part of the
 documentation.
 
-See also: :ref:`public api`_.
+See also: :ref:`public api`
 
 Top-level functions
 ===================
@@ -117,6 +117,9 @@ Indexing
    Dataset.loc
    Dataset.isel
    Dataset.sel
+   Dataset.head
+   Dataset.tail
+   Dataset.thin
    Dataset.squeeze
    Dataset.interp
    Dataset.interp_like
@@ -279,6 +282,9 @@ Indexing
    DataArray.loc
    DataArray.isel
    DataArray.sel
+   DataArray.head
+   DataArray.tail
+   DataArray.thin
    DataArray.squeeze
    DataArray.interp
    DataArray.interp_like
@@ -604,6 +610,7 @@ Plotting
 
    Dataset.plot
    DataArray.plot
+   Dataset.plot.scatter
    plot.plot
    plot.contourf
    plot.contour

doc/dask.rst

@@ -75,13 +75,14 @@ entirely equivalent to opening a dataset using ``open_dataset`` and then
 chunking the data using the ``chunk`` method, e.g.,
 ``xr.open_dataset('example-data.nc').chunk({'time': 10})``.
 
-To open multiple files simultaneously, use :py:func:`~xarray.open_mfdataset`::
+To open multiple files simultaneously in parallel using Dask delayed,
+use :py:func:`~xarray.open_mfdataset`::
 
-    xr.open_mfdataset('my/files/*.nc')
+    xr.open_mfdataset('my/files/*.nc', parallel=True)
 
 This function will automatically concatenate and merge dataset into one in
 the simple cases that it understands (see :py:func:`~xarray.auto_combine`
-for the full disclaimer). By default, ``open_mfdataset`` will chunk each
+for the full disclaimer). By default, :py:func:`~xarray.open_mfdataset` will chunk each
 netCDF file into a single Dask array; again, supply the ``chunks`` argument to
 control the size of the resulting Dask arrays. In more complex cases, you can
 open each file individually using ``open_dataset`` and merge the result, as
@@ -132,6 +133,13 @@ A dataset can also be converted to a Dask DataFrame using :py:meth:`~xarray.Data
 
 Dask DataFrames do not support multi-indexes so the coordinate variables from the dataset are included as columns in the Dask DataFrame.
 
+.. ipython:: python
+    :suppress:
+
+    import os
+    os.remove('example-data.nc')
+    os.remove('manipulated-example-data.nc')
+
 Using Dask with xarray
 ----------------------
 
@@ -373,12 +381,6 @@ one million elements (e.g., a 1000x1000 matrix). With large arrays (10+ GB), the
 cost of queueing up Dask operations can be noticeable, and you may need even
 larger chunksizes.
 
-.. ipython:: python
-    :suppress:
-
-    import os
-    os.remove('example-data.nc')
-
 Optimization Tips
 -----------------
 

doc/gallery/plot_cartopy_facetgrid.py

@@ -41,6 +41,6 @@
     ax.set_extent([-160, -30, 5, 75])
     # Without this aspect attributes the maps will look chaotic and the
     # "extent" attribute above will be ignored
-    ax.set_aspect("equal", "box-forced")
+    ax.set_aspect("equal")
 
 plt.show()

doc/indexing.rst

@@ -236,9 +236,8 @@ The :py:meth:`~xarray.Dataset.drop` method returns a new object with the listed
 index labels along a dimension dropped:
 
 .. ipython:: python
-    :okwarning:
 
-    ds.drop(['IN', 'IL'], dim='space')
+    ds.drop(space=['IN', 'IL'])
 
 ``drop`` is both a ``Dataset`` and ``DataArray`` method.
 
@@ -393,14 +392,6 @@ These methods may also be applied to ``Dataset`` objects
   You may find increased performance by loading your data into memory first,
   e.g., with :py:meth:`~xarray.Dataset.load`.
 
-.. note::
-
-  Vectorized indexing is a new feature in v0.10.
-  In older versions of xarray, dimensions of indexers are ignored.
-  Dedicated methods for some advanced indexing use cases,
-  ``isel_points`` and ``sel_points`` are now deprecated.
-  See :ref:`more_advanced_indexing` for their alternative.
-
 .. note::
 
   If an indexer is a :py:meth:`~xarray.DataArray`, its coordinates should not